Multi-Modal Motion-Capture-Based Biometric Systems for Emergency Response and Patient Rehabilitation

2021 ◽  
pp. 653-678
Author(s):  
Marina L. Gavrilova ◽  
Ferdous Ahmed ◽  
A. S. M. Hossain Bari ◽  
Ruixuan Liu ◽  
Tiantian Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Body Movement ◽  
Gait Recognition ◽  
Medical Patient ◽  
Recognition System ◽  
Svm Classifier ◽  
Kinect Sensor ◽  
Open Problems ◽  
Emotion Identification ◽  
Emotional Cues

This chapter outlines the current state of the art of Kinect sensor gait and activity authentication. It also focuses on emotional cues that could be observed from human body and posture. It presents a prototype of a system that combines recently developed behavioral gait and posture recognition methods for human emotion identification. A backbone of the system is Kinect sensor gait recognition, which explores the relationship between joint-relative angles and joint-relative distances through machine learning. The chapter then introduces a real-time gesture recognition system developed using Kinect sensor and trained with SVM classifier. Preliminary experimental results demonstrate accuracy and feasibility of using such systems in real-world scenarios. While gait and emotion from body movement has been researched in the context of standalone biometric security systems, they were never previously explored for physiotherapy rehabilitation and real-time patient feedback. The survey of recent progress and open problems in crucial areas of medical patient rehabilitation and rescue operations conclude this chapter.

Multi-Modal Motion-Capture-Based Biometric Systems for Emergency Response and Patient Rehabilitation

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Marina L. Gavrilova ◽  
Ferdous Ahmed ◽  
A. S. M. Hossain Bari ◽  
Ruixuan Liu ◽  
Tiantian Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Body Movement ◽  
Gait Recognition ◽  
Medical Patient ◽  
Recognition System ◽  
Svm Classifier ◽  
Kinect Sensor ◽  
Open Problems ◽  
Emotion Identification ◽  
Emotional Cues

This chapter outlines the current state of the art of Kinect sensor gait and activity authentication. It also focuses on emotional cues that could be observed from human body and posture. It presents a prototype of a system that combines recently developed behavioral gait and posture recognition methods for human emotion identification. A backbone of the system is Kinect sensor gait recognition, which explores the relationship between joint-relative angles and joint-relative distances through machine learning. The chapter then introduces a real-time gesture recognition system developed using Kinect sensor and trained with SVM classifier. Preliminary experimental results demonstrate accuracy and feasibility of using such systems in real-world scenarios. While gait and emotion from body movement has been researched in the context of standalone biometric security systems, they were never previously explored for physiotherapy rehabilitation and real-time patient feedback. The survey of recent progress and open problems in crucial areas of medical patient rehabilitation and rescue operations conclude this chapter.

scholarly journals Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

2021 ◽  
Author(s):  
Linghui Xu ◽  
Jiansong Chen ◽  
Fei Wang ◽  
Yuting Chen ◽  
Wei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Plantar Pressure ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Gait Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Time Performance ◽  
Pathological Gait

Abstract Background: Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information.Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of seventeen children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe in, toe out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified 10-fold cross-validation with recall, precision, and a time cost as metrics.Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% recognition accuracy respectively in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing.Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

Centre-of-mass-based gait recognition for person identification

2019 ◽  
Vol 13 ◽  
Author(s):  
Rajib Ghosh
Keyword(s):  
Body Movement ◽  
Gait Recognition ◽  
Visual Surveillance ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification System ◽  
Centre Of Mass ◽  
Person Identification ◽  
Walking Pattern ◽  
Walking Activity

Background: Gait recognition focuses on identification of persons from their walking activity. This type of system plays an important role in visual surveillance applications. Walking pattern of every person is unique and difficult to replicate by others. Objective: The present article focuses on to develop a person identification system based on gait recognition. Methods: In this article, a novel gait recognition approach is proposed to show how human body Centre-of-mass-based walking characteristics can be used to recognize unauthorized and suspicious persons when they enter in a surveillance area. Walking pattern varies from person to person mainly due to the differences in the footsteps and body movement. Initially, background is modelled from the input video captured through static cameras deployed for security purpose. Foreground moving object in the individual frames are then segmented using the background subtraction algorithm. Centre-of-mass based discriminative features of various walking patterns are then studied using Support Vector Machine(SVM) classifier to identify each unique walking pattern. Results: The proposed system has been evaluated using a self-generated dataset containing side view of various walking video clips. The experimental results demonstrate that the proposed system achieves an encouraging person identification rate. Conclusion: This work can be further extended to provide a general approach in developing an automatic person identification system in unconstrained environment.

scholarly journals Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Linghui Xu ◽  
Jiansong Chen ◽  
Fei Wang ◽  
Yuting Chen ◽  
Wei Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Plantar Pressure ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Gait Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Time Performance ◽  
Pathological Gait

Abstract Background Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information. Methods In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics. Results The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing. Conclusions In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

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